epochs

Mailing List Archive (read-only)
1

External Email - Use Caution

Dear experts:
I am trying to run a tutorial recommended. https://mne.tools/dev/auto_tutorials/preprocessing/plot_20_rejecting_bad_data.html#rejecting-epochs-based-on-channel-amplitude

My data is not from the tutorial. It is an EEG. Also I have this outputs. Of course I use evoked = epochs.average() and it says less than one epoch. What can I do?

print(events)

[[ 0 0 65536]
[ 18275 0 128]
[ 19387 0 2]
[ 20422 0 2]
[ 32156 0 128]
[ 46029 0 128]
[ 46873 0 2]
[ 47522 0 4]
[ 72924 0 128]
[ 73666 0 2]
[ 74230 0 2]
[ 92717 0 128]
[ 94025 0 2]
[ 94590 0 2]
[108211 0 128]
[109532 0 2]
[110110 0 4]
[130866 0 128]
[131605 0 4]
[132900 0 2]
[156301 0 128]
[157153 0 2]
[157843 0 4]
[176353 0 128]
[177182 0 2]
[177821 0 2]
[191436 0 128]
[192495 0 4]
[193129 0 2]
[233638 0 128]
[234323 0 4]
[234936 0 4]
[248375 0 128]
[249218 0 2]
[249817 0 2]
[255773 0 128]
[256493 0 2]
[257060 0 4]
[286302 0 128]
[287009 0 4]
[287601 0 2]
[320684 0 128]
[321413 0 4]
[340579 0 128]
[341369 0 4]
[342650 0 2]
[383286 0 128]
[384166 0 2]
[384810 0 2]
[406476 0 128]
[407297 0 2]
[407956 0 4]
[409017 0 2]
[444348 0 128]
[445107 0 2]
[445683 0 4]
[446969 0 2]
[482043 0 128]
[482761 0 2]
[483421 0 2]
[521536 0 128]
[522365 0 2]
[523009 0 2]
[535415 0 128]
[536091 0 4]
[536691 0 2]
[573609 0 128]
[574341 0 4]
[574916 0 2]
[588192 0 128]
[588973 0 4]
[589524 0 2]
[611318 0 128]
[612110 0 2]
[612703 0 4]
[613416 0 2]
[634153 0 128]
[635029 0 2]
[635592 0 2]
[636193 0 2]
[662819 0 128]
[663674 0 2]
[664355 0 4]
[665003 0 2]
[677292 0 128]
[678266 0 2]
[678926 0 2]
[679619 0 2]]

epochs = mne.Epochs(raw, events, event_id=dict(aud=65536, vis=2, aud2=4, vis2=128), tmin=-0.2, tmax=0.5,reject=dict(eeg=100e-6), flat=dict(eeg=1e-6),preload=True)

88 matching events found
Applying baseline correction (mode: mean)
Not setting metadata
0 projection items activated
Loading data for 88 events and 180 original time points ...

?

88 bad epochs dropped

Sincerely,
Andrade.
-------------- next part --------------
An HTML attachment was scrubbed...
URL: http://mail.nmr.mgh.harvard.edu/pipermail/mne_analysis/attachments/20200414/08c06604/attachment-0001.html

2

External Email - Use Caution

You can look at `epochs.drop_log` or `epochs.plot_drop_log` to see why. See
for example:

https://mne.tools/dev/auto_tutorials/epochs/plot_10_epochs_overview.html#creating-epoched-data-from-a-raw-object

Eric

3

External Email - Use Caution

yes I did (epochs.plot_drop_log) but I just see a lot of columns with 100%. From where I deduce all channels are dropped. I don?t know what explanation for that.

        External Email - Use Caution

You can look at `epochs.drop_log` or `epochs.plot_drop_log` to see why. See for example:

https://mne.tools/dev/auto_tutorials/epochs/plot_10_epochs_overview.html#creating-epoched-data-from-a-raw-object

Eric

        External Email - Use Caution

Dear experts:
I am trying to run a tutorial recommended. Page Redirection

My data is not from the tutorial. It is an EEG. Also I have this outputs. Of course I use evoked = epochs.average() and it says less than one epoch. What can I do?

print(events)

[[ 0 0 65536]
[ 18275 0 128]
[ 19387 0 2]
[ 20422 0 2]
[ 32156 0 128]
[ 46029 0 128]
[ 46873 0 2]
[ 47522 0 4]
[ 72924 0 128]
[ 73666 0 2]
[ 74230 0 2]
[ 92717 0 128]
[ 94025 0 2]
[ 94590 0 2]
[108211 0 128]
[109532 0 2]
[110110 0 4]
[130866 0 128]
[131605 0 4]
[132900 0 2]
[156301 0 128]
[157153 0 2]
[157843 0 4]
[176353 0 128]
[177182 0 2]
[177821 0 2]
[191436 0 128]
[192495 0 4]
[193129 0 2]
[233638 0 128]
[234323 0 4]
[234936 0 4]
[248375 0 128]
[249218 0 2]
[249817 0 2]
[255773 0 128]
[256493 0 2]
[257060 0 4]
[286302 0 128]
[287009 0 4]
[287601 0 2]
[320684 0 128]
[321413 0 4]
[340579 0 128]
[341369 0 4]
[342650 0 2]
[383286 0 128]
[384166 0 2]
[384810 0 2]
[406476 0 128]
[407297 0 2]
[407956 0 4]
[409017 0 2]
[444348 0 128]
[445107 0 2]
[445683 0 4]
[446969 0 2]
[482043 0 128]
[482761 0 2]
[483421 0 2]
[521536 0 128]
[522365 0 2]
[523009 0 2]
[535415 0 128]
[536091 0 4]
[536691 0 2]
[573609 0 128]
[574341 0 4]
[574916 0 2]
[588192 0 128]
[588973 0 4]
[589524 0 2]
[611318 0 128]
[612110 0 2]
[612703 0 4]
[613416 0 2]
[634153 0 128]
[635029 0 2]
[635592 0 2]
[636193 0 2]
[662819 0 128]
[663674 0 2]
[664355 0 4]
[665003 0 2]
[677292 0 128]
[678266 0 2]
[678926 0 2]
[679619 0 2]]

epochs = mne.Epochs(raw, events, event_id=dict(aud=65536, vis=2, aud2=4, vis2=128), tmin=-0.2, tmax=0.5,reject=dict(eeg=100e-6), flat=dict(eeg=1e-6),preload=True)

88 matching events found
Applying baseline correction (mode: mean)
Not setting metadata
0 projection items activated
Loading data for 88 events and 180 original time points ...

?

88 bad epochs dropped

Sincerely,
Andrade.

4

External Email - Use Caution

I just saw that you can do epochs out of raw, events, and tmin tax

        External Email - Use Caution

You can look at `epochs.drop_log` or `epochs.plot_drop_log` to see why. See for example:

https://mne.tools/dev/auto_tutorials/epochs/plot_10_epochs_overview.html#creating-epoched-data-from-a-raw-object

Eric

        External Email - Use Caution

Dear experts:
I am trying to run a tutorial recommended. Page Redirection

My data is not from the tutorial. It is an EEG. Also I have this outputs. Of course I use evoked = epochs.average() and it says less than one epoch. What can I do?

print(events)

[[ 0 0 65536]
[ 18275 0 128]
[ 19387 0 2]
[ 20422 0 2]
[ 32156 0 128]
[ 46029 0 128]
[ 46873 0 2]
[ 47522 0 4]
[ 72924 0 128]
[ 73666 0 2]
[ 74230 0 2]
[ 92717 0 128]
[ 94025 0 2]
[ 94590 0 2]
[108211 0 128]
[109532 0 2]
[110110 0 4]
[130866 0 128]
[131605 0 4]
[132900 0 2]
[156301 0 128]
[157153 0 2]
[157843 0 4]
[176353 0 128]
[177182 0 2]
[177821 0 2]
[191436 0 128]
[192495 0 4]
[193129 0 2]
[233638 0 128]
[234323 0 4]
[234936 0 4]
[248375 0 128]
[249218 0 2]
[249817 0 2]
[255773 0 128]
[256493 0 2]
[257060 0 4]
[286302 0 128]
[287009 0 4]
[287601 0 2]
[320684 0 128]
[321413 0 4]
[340579 0 128]
[341369 0 4]
[342650 0 2]
[383286 0 128]
[384166 0 2]
[384810 0 2]
[406476 0 128]
[407297 0 2]
[407956 0 4]
[409017 0 2]
[444348 0 128]
[445107 0 2]
[445683 0 4]
[446969 0 2]
[482043 0 128]
[482761 0 2]
[483421 0 2]
[521536 0 128]
[522365 0 2]
[523009 0 2]
[535415 0 128]
[536091 0 4]
[536691 0 2]
[573609 0 128]
[574341 0 4]
[574916 0 2]
[588192 0 128]
[588973 0 4]
[589524 0 2]
[611318 0 128]
[612110 0 2]
[612703 0 4]
[613416 0 2]
[634153 0 128]
[635029 0 2]
[635592 0 2]
[636193 0 2]
[662819 0 128]
[663674 0 2]
[664355 0 4]
[665003 0 2]
[677292 0 128]
[678266 0 2]
[678926 0 2]
[679619 0 2]]

epochs = mne.Epochs(raw, events, event_id=dict(aud=65536, vis=2, aud2=4, vis2=128), tmin=-0.2, tmax=0.5,reject=dict(eeg=100e-6), flat=dict(eeg=1e-6),preload=True)

88 matching events found
Applying baseline correction (mode: mean)
Not setting metadata
0 projection items activated
Loading data for 88 events and 180 original time points ...

?

88 bad epochs dropped

Sincerely,
Andrade.